Liquid loading in natural gas wells is one of the main causes of decline and eventual cease of production. Identifying the onset of liquid loading and its impacts on production is vital in production optimization of gas wells. In this study, a new state-of-the-art experimental facility is used to analyze two-phase flow parameters, such as liquid and gas velocities, pressure drop, liquid holdup, and flow pattern, and study the onset of liquid loading. Experiments are conducted using air and water in a 0.0508-m (2-in.) internal diameter (ID) vertical flow loop. The facility is designed to eliminate any source of disruption to the flow in a temperature-controlled indoor environment to avoid changes in fluid properties. A wide range of data are collected related to the onset of liquid loading and in the churn region. The acquired data are compared with other studies to ensure the accuracy and repeatability of the tests.

A quantitative method is developed to predict the onset of liquid loading, namely, the positive frictional pressure gradient (negative pressure drop). A comparison is made between the visual observations for flow pattern transition, positive frictional pressure gradient, and minimum pressure drop. Overall, the results show that the positive frictional pressure gradient approach can provide a better estimation of the onset of liquid loading than the minimum pressure drop approach. Additionally, Tulsa University Fluid Flow Project (TUFFP) unified model (v2016) and OLGA (v2016.2.1) are used to evaluate the average pressure drop, liquid holdup, and flow pattern. Furthermore, the liquid film reversal model, liquid droplet models, and inflection point approach are included in the comparison of the onset of liquid loading with experimental data. The OLGA model predicts the results more precisely than other models. The comparison with the experimental data indicates that inaccurate flow pattern predictions could significantly increase the relative errors in pressure drop and liquid holdup. In addition, the evaluation of OLGA results suggests the need to develop a model for churn flow, as the relative error increases sharply in the churn flow with the model predicting the transition to slug flow.

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